The conflicts among parsers can confuse users on the parser to use for the downstream tasks, as the performance of different parsers can vary significantly on different domains and languages. No parser can consistently achieve the best results on all datasets, and it is costly and impractical for users to obtain ground truth parsing results. Table REF  shows the percentage of agreement among the structure of the parsers' outputs on six benchmark datasets, including Penn Treebank-3 [1]}, OntoNotes (English and Chinese) [2]}, Genia [3]}, French Treebank [4]}, and TIGER Corpus [5]}. We execute four parsers including Berkeley [6]}, CoreNLP [7]}, AllenNLP [8]}, and Hanlp [9]}, for the English datasets, and three parsers, namely Berkeley, CoreNLP, and Hanlp, for the non-English datasets. On the Penn Treebank-3 dataset, it can be observed that all the parsers agree only on 1.32% of the sentences. A similar observation can be made for other datasets.
